Network challenges of new sources of big data
Big data is a term used to describe data sets so large and/or complex that traditional data processing algorithms and applications are inadequate. There are numerous challenges when handling these data including analysis, capture, search, sharing, storage, transfer, visualization and privacy. A significant portion of big data is expected to be generated by special types of networks -- wireless sensor systems. In this paper we first introduce the concept of big data and then proceed describing the current state-of-the-art in potential sources of big that may appear in the coming future including electromagnetic THz and bacterial micro- and nanoscale networks. We summarize the recent progress in these systems highlighting the problems that should be solved to make them realistic and contribute to the large big data picture.
Full texts of third international conference on data analytics are presented.
Everyone is talking about big data, and how it will transform go vernment. However, looking past the excitement, questions abound. How to use big data to make intelligent decisions? Perh aps most importantly, what value will it really deliver to the government and the citizenry it serves to? By reviewing the literatu re and summarizing insights from a series of business reports and interviews of public sector and top companies Chief Information Officers (CIOs), we offer a survey for both practitioners and researchers inter ested in understanding big data in the public sector of Russian Federation. Remarkable changes are taking place in IT industry of Russian Federati on at present: new strategies of Federal Government, sanctions and import substitution tendency. The paper makes the estimate of internal and external factors, which effect on big data development in public sector of Russian Federation and makes comparative analysis of Russian and world practices of the study area.
In this paper special data structure for big social graph storing and operating is presented. We discuss mainly graph paths searching, obtaining subgrapths and addition of new edges and vertices.
The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complexity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.
The proceedings of the 11th International Conference on Service-Oriented Computing (ICSOC 2013), held in Berlin, Germany, December 2–5, 2013, contain high-quality research papers that represent the latest results, ideas, and positions in the field of service-oriented computing. Since the first meeting more than ten years ago, ICSOC has grown to become the premier international forum for academics, industry researchers, and practitioners to share, report, and discuss their ground-breaking work. ICSOC 2013 continued along this tradition, in particular focusing on emerging trends at the intersection between service-oriented, cloud computing, and big data.
In 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the momentIn 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the moment
Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.